Analytics in the Cloud: Getting The Most Out Of Analytics Deployments

Cloud Analytics: Getting the most

out of cloud implementationsApril 2016

Lyndsay Wise, Research

Director, EMA

Jeff Kelly, Data Evangelist,

Pivotal

Ian Andrews, VP of

Products, Pivotal

Today’s Speakers

Submit your questions to the panel using:

@wiseAnalytics @jeffreyfkelly #cloudAnalytics

© 2016, Enterprise Management Associates

Join the Conversation

NEED STANDARD WEBINAR LOGISTICS SLIDE HERE

• Business Drivers and Perception of

Cloud Analytics

• Financial Drivers and TCO

• Business Agility and Smart Applications

• Cloud Analytics Success factors

• Cloud Analytics Use Cases

• Summary & Q&A

Agenda for Today’s Webinar

Topic 1: Business Drivers and

Perceptions of Cloud Analytics

Cloud is better They are the

same

On-premises is

better

Ease of Adoption 47.1% 43.3% 9.6%

Ease of Technical Distribution 48.6% 38.9% 12.5%

Functionality 45.7% 44.2% 10.1%

Time to Implementation 47.6% 41.3% 11.1%

Total Cost of Ownership 53.4% 35.6% 11.1%

Perceptions of Cloud vs. On-Premises Use

42.8%

Data security

37.5%Lower

administration 33.7%Ease of

integration

Cloud Benefits

Infrastructure1

Platform2

Software3

A SEPARATION OF CONCERNS

Modern cloud platforms enable software to

be developed & deployed without regard

to the underlying infrastructure

Modern Software Methodology Modern Cloud Platform

BEING A CLOUD NATIVE COMPANY

Is about building high-quality software—at start-up speed—

leveraging a modern cloud platform, a modern development

process, and the power of Big Data to continuously drive

innovation

Big Data

Stream + Batch Processing

Programming + Operating Model

Cloud-Native Platform

Microservices FrameworkPlatform RuntimeHadoop

DW

Spark

Microservices and Polyglot Persistence

IMDG

K/V Store

Relational DB

Big Data &

Machine Learning

Modern Cloud-Native Data Architecture

Cloud Infrastructure

18.6%Better information

visibility/higher

efficiencies

16.6%Improved speed to

implementation

Top Business Drivers

Speed of Deployment

Speed to Insight

Agility and Adaptability

Data Gravity/Data Movement

Topic 2: Financial Drivers and TCO

Why Organizations are Looking at Cloud

19.0%Time savings

18.4%ROI

17.0%Effective use of

biz resources

Delivering Value Through Cloud – Moving

Towards Revenue Growth

Top Financial Drivers and TCO

Great software companies leverage Big Data

to fundamentally change the consumer

experience and pioneer entirely new

business models

DISRUPTIVE AND CONVERGING TRENDS IN BIG DATA

(Data)

Microservices

Loosely coupled

services architecture,

bounded by context

Cloud-Native

Platforms

Enabling continuous

delivery & automated

operations

Open Source

Database

Innovation

Extreme scale &

performance advantages,

built for the cloud

Machine

Learning

Use of predictive

analytics to build

smart apps

Topic 3: Business Agility and Smart

Applications

Importance of Agility

Business Goals

Scale-out

analytic

database

Model API Cloud Native

Application

Platform

Data

Sources

0 5

Smart Apps: Models Manifesting as Microservices

1) Engage

2) Capture3) Model

4) Suggest

A Recipe for Smart Apps

Topic 4: Cloud Success Factors

64.8%Successful

3.3 %Not successful

13.7%Neither successful,

nor unsuccessful

Success Levels of Cloud Deployments

Levels of Technical Success

Workloads

AppDevelopment

Data analytics

Cloud-nativeApp platform

Data Science & Model building

DataMicroservice

AP

P Must support scale-out

query processingMust deliver as an API

Must embrace agile development,

focus on outcomes

Must support

microservices, agile dev, and

connect to big data analytics

A Real-World Example

Topic 5: Cloud Use Cases

Summary and Q&A

Question and Answer

http://enterprisemanagement.com/ http://springoneplatform.io/

More resources at pivotal.io/resources and https://blog.pivotal.io/

Resources

Let’s build something

MEANINGFUL